OpenRaiser/Intern-Atlas huggingface dataset:
On Erasure Broadcast Channels with Hard Deadlines
Venue: arXiv cs.NI 2018
Year: 2018

This paper considers packet scheduling over a broadcast channel with packet erasures to multiple receivers with different messages (multiple uni-cast) each with possibly different hard deadline constraints. A novel metric is proposed and evaluated: the global deadline outage probability, which gives the probability that the hard communication deadline is not met for at least one of the receivers. The cut-set upper bound is derived and a scheduling policy is proposed to determine which receiver's packets should be sent in each time slot. This policy is shown to be optimal among all scheduling policies, i.e., it achieves all boundary points of cut-set upper bounds when the transmitter knows the erasure patterns for all the receivers ahead of making the scheduling decision. An expression for the global deadline outage probability is obtained for two receivers and is plotted and interpreted for various system parameters. These plots are not Monte-Carlo simulations, and hence the obtained expression may be used in the design of future downlink broadcast networks. Future extensions to per-user deadline outage probabilities as well as to scenarios with causal knowledge of the channel states are briefly discussed.
Security against false data injection attack in cyber-physical systems
Venue: arXiv cs.CR 2018
Year: 2018

In this paper, secure, remote estimation of a linear Gaussian process via observations at multiple sensors is considered. Such a framework is relevant to many cyber-physical systems and internet-of-things applications. Sensors make sequential measurements that are shared with a fusion center; the fusion center applies a certain filtering algorithm to make its estimates. The challenge is the presence of a few unknown malicious sensors which can inject anomalous observations to skew the estimates at the fusion center. The set of malicious sensors may be time-varying. The problems of malicious sensor detection and secure estimation are considered. First, an algorithm for secure estimation is proposed. The proposed estimation scheme uses a novel filtering and learning algorithm, where an optimal filter is learnt over time by using the sensor observations in order to filter out malicious sensor observations while retaining other sensor measurements. Next, a novel detector to detect injection attacks on an unknown sensor subset is developed. Numerical results demonstrate up to 3 dB gain in the mean squared error and up to 75% higher attack detection probability under a small false alarm rate constraint, against a competing algorithm that requires additional side information.
Multimodal Deep Domain Adaptation
Venue: arXiv cs.LG 2018
Year: 2018

Typically a classifier trained on a given dataset (source domain) does not performs well if it is tested on data acquired in a different setting (target domain). This is the problem that domain adaptation (DA) tries to overcome and, while it is a well explored topic in computer vision, it is largely ignored in robotic vision where usually visual classification methods are trained and tested in the same domain. Robots should be able to deal with unknown environments, recognize objects and use them in the correct way, so it is important to explore the domain adaptation scenario also in this context. The goal of the project is to define a benchmark and a protocol for multi-modal domain adaptation that is valuable for the robot vision community. With this purpose some of the state-of-the-art DA methods are selected: Deep Adaptation Network (DAN), Domain Adversarial Training of Neural Network (DANN), Automatic Domain Alignment Layers (AutoDIAL) and Adversarial Discriminative Domain Adaptation (ADDA). Evaluations have been done using different data types: RGB only, depth only and RGB-D over the following datasets, designed for the robotic community: RGB-D Object Dataset (ROD), Web Object Dataset (WOD), Autonomous Robot Indoor Dataset (ARID), Big Berkeley Instance Recognition Dataset (BigBIRD) and Active Vision Dataset. Although progresses have been made on the formulation of effective adaptation algorithms and more realistic object datasets are available, the results obtained show that, training a sufficiently good object classifier, especially in the domain adaptation scenario, is still an unsolved problem. Also the best way to combine depth with RGB informations to improve the performance is a point that needs to be investigated more.
Compact Convolutional Neural Networks for Multi-Class, Personalised, Closed-Loop EEG-BCI
Venue: arXiv cs.HC 2018
Year: 2018

For many people suffering from motor disabilities, assistive devices controlled with only brain activity are the only way to interact with their environment. Natural tasks often require different kinds of interactions, involving different controllers the user should be able to select in a self-paced way. We developed a Brain-Computer Interface (BCI) allowing users to switch between four control modes in a self-paced way in real-time. Since the system is devised to be used in domestic environments in a user-friendly way, we selected non-invasive electroencephalographic (EEG) signals and convolutional neural networks (CNNs), known for their ability to find the optimal features in classification tasks. We tested our system using the Cybathlon BCI computer game, which embodies all the challenges inherent to real-time control. Our preliminary results show that an efficient architecture (SmallNet), with only one convolutional layer, can classify 4 mental activities chosen by the user. The BCI system is run and validated online. It is kept up-to-date through the use of newly collected signals along playing, reaching an online accuracy of 47.6% where most approaches only report results obtained offline. We found that models trained with data collected online better predicted the behaviour of the system in real-time. This suggests that similar (CNN based) offline classifying methods found in the literature might experience a drop in performance when applied online. Compared to our previous decoder of physiological signals relying on blinks, we increased by a factor 2 the amount of states among which the user can transit, bringing the opportunity for finer control of specific subtasks composing natural grasping in a self-paced way. Our results are comparable to those shown at the Cybathlon's BCI Race but further improvements on accuracy are required.
t-SNE-CUDA: GPU-Accelerated t-SNE and its Applications to Modern Data
Venue: arXiv cs.LG 2018
Year: 2018

Modern datasets and models are notoriously difficult to explore and analyze due to their inherent high dimensionality and massive numbers of samples. Existing visualization methods which employ dimensionality reduction to two or three dimensions are often inefficient and/or ineffective for these datasets. This paper introduces t-SNE-CUDA, a GPU-accelerated implementation of t-distributed Symmetric Neighbor Embedding (t-SNE) for visualizing datasets and models. t-SNE-CUDA significantly outperforms current implementations with 50-700x speedups on the CIFAR-10 and MNIST datasets. These speedups enable, for the first time, visualization of the neural network activations on the entire ImageNet dataset - a feat that was previously computationally intractable. We also demonstrate visualization performance in the NLP domain by visualizing the GloVe embedding vectors. From these visualizations, we can draw interesting conclusions about using the L2 metric in these embedding spaces. t-SNE-CUDA is publicly available athttps://github.com/CannyLab/tsne-cuda
Open Source Android Vulnerability Detection Tools: A Survey
Venue: arXiv cs.CR 2018
Year: 2018

Since last decade, smartphones have become an integral part of everyone's life. Having the ability to handle many useful and attractive applications, smartphones sport flawless functionality and small sizes leading to their exponential growth. Additionally, due to the huge user base and a wide range of functionalities, these mobile platforms have become a popular source of information to the public through several Apps provided by the DHS Citizen Application Directory. Such wide audience to this platform is also making it a huge target for cyber- attacks. While Android, the most popular open source mobile platform, has its base set of permissions to protect the device and resources, it does not provide a security framework to defend against any attack. This paper surveys threat, vulnerability and security analysis tools, which are open source in nature, for the Android platform and systemizes the knowledge of Android security mechanisms. Additionally, a comparison of three popular tools is presented.
Cyber-attack Mitigation and Impact Analysis for Low-power IoT Devices
Venue: arXiv cs.CR 2018
Year: 2018

Recent years have seen exponential development in wireless sensor devices and their rebirth as IoT, as well as increased popularity in wireless home devices such as bulbs, fans, and microwave. As they can be used in various fields such as medical devices, environmental studies, fire department or military application, etc., security of these low powered devices will always be a concern for all the user and security experts. Users nowadays want to control all these "smart" wireless home devices through their smartphones using an internet connection. Attacks such as distributed attacks on these devices will render the whole system vulnerable as these attacks can record and extract confidential information as well as increase resource (energy) consumption of the entire network. In this paper, we propose a cyber-attack detection algorithm and present an impact analysis of easy-to-launch cyber-attacks on a low-power mote (Z1 Zolertia) as a model IoT device. We also present detailed results of power consumption analysis with and without attack along with when the mitigation algorithm for intrusion detection is implemented.
FADE: Fast and Asymptotically efficient Distributed Estimator for dynamic networks
Venue: arXiv cs.SY 2018
Year: 2018

Consider a set of agents that wish to estimate a vector of parameters of their mutual interest. For this estimation goal, agents can sense and communicate. When sensing, an agent measures (in additive gaussian noise) linear combinations of the unknown vector of parameters. When communicating, an agent can broadcast information to a few other agents, by using the channels that happen to be randomly at its disposal at the time. To coordinate the agents towards their estimation goal, we propose a novel algorithm called FADE (Fast and Asymptotically efficient Distributed Estimator), in which agents collaborate at discrete time-steps; at each time-step, agents sense and communicate just once, while also updating their own estimate of the unknown vector of parameters. FADE enjoys five attractive features: first, it is an intuitive estimator, simple to derive; second, it withstands dynamic networks, that is, networks whose communication channels change randomly over time; third, it is strongly consistent in that, as time-steps play out, each agent's local estimate converges (almost surely) to the true vector of parameters; fourth, it is both asymptotically unbiased and efficient, which means that, across time, each agent's estimate becomes unbiased and the mean-square error (MSE) of each agent's estimate vanishes to zero at the same rate of the MSE of the optimal estimator at an almighty central node; fifth, and most importantly, when compared with a state-of-art consensus+innovation (CI) algorithm, it yields estimates with outstandingly lower mean-square errors, for the same number of communications -- for example, in a sparsely connected network model with 50 agents, we find through numerical simulations that the reduction can be dramatic, reaching several orders of magnitude.
Graph-Based Recommendation System
Venue: arXiv cs.IR 2018
Year: 2018

In this work, we study recommendation systems modelled as contextual multi-armed bandit (MAB) problems. We propose a graph-based recommendation system that learns and exploits the geometry of the user space to create meaningful clusters in the user domain. This reduces the dimensionality of the recommendation problem while preserving the accuracy of MAB. We then study the effect of graph sparsity and clusters size on the MAB performance and provide exhaustive simulation results both in synthetic and in real-case datasets. Simulation results show improvements with respect to state-of-the-art MAB algorithms.
New Analysis Techniques for Supporting Hard Real-Time Sporadic DAG Task Systems on Multiprocessors
Venue: arXiv cs.OS 2018
Year: 2018

The scheduling and schedulability analysis of real-time directed acyclic graph (DAG) task systems have received much recent attention. The DAG model can accurately represent intra-task parallelim and precedence constraints existing in many application domains. Existing techniques show that analyzing the DAG model is fundamentally more challenging compared to the ordinary sporadic task model, due to the complex intra-DAG precedence constraints which may cause rather pessimistic schedulability loss. However,such increased loss is counter-intuitive because the DAG structure shall better exploit the parallelism provided by the multiprocessor platform. Our observation is that the intra-DAG precedence constraints, if not carefully considered by the scheduling algorithm, may cause very unpredictable execution behaviors of subtasks in a DAG and further cause pessimistic analysis. In this paper, we present a set of novel scheduling and analysis techniques for better supporting hard real-time sporadic DAG tasks on multiprocessors, through smartly defining and analyzing the execution order of subtasks in each DAG. Evaluation demonstrates that our developed utilization-based schedulability test is highly efficient, which dramatically improves schedulability of existing utilization-based tests by over 60% on average. Interestingly, when each DAG in the system is an ordinary sporadic task, our test becomes identical to the classical density test designed for the sporadic task model.
Emulating Hybrid Memory on NUMA Hardware
Venue: arXiv cs.PL 2018
Year: 2018

Non-volatile memory (NVM) has the potential to disrupt the boundary between memory and storage, including the abstractions that manage this boundary. Researchers comparing the speed, durability, and abstractions of hybrid systems with DRAM, NVM, and disk to traditional systems typically use simulation, which makes it easy to evaluate different hardware technologies and parameters. Unfortunately, simulation is extremely slow, limiting the number of applications and dataset sizes in the evaluation. Simulation typically precludes realistic multiprogram workloads and considering runtime and operating system design alternatives. Good methodology embraces a variety of techniques for validation, expanding the experimental scope, and uncovering new insights. This paper introduces an emulation platform for hybrid memory that uses commodity NUMA servers. Emulation complements simulation well, offering speed and accuracy for realistic workloads, and richer software experimentation. We use a thread-local socket to emulate DRAM and the remote socket to emulate NVM. We use standard C library routines to allocate heap memory in the DRAM or NVM socket for use with explicit memory management or garbage collection. We evaluate the emulator using various configurations of write-rationing garbage collectors that improve NVM lifetimes by limiting writes to NVM, and use 15 applications from three benchmark suites with various datasets and workload configurations. We show emulation enhances simulation results. The two systems confirm most trends, such as NVM write and read rates of different software configurations, increasing our confidence for predicting future system effects. Emulation adds novel insights, such as the non-linear effects of multi-program workloads on write rates.
Multimodal Sensing and Interaction for a Robotic Hand Orthosis
Venue: arXiv cs.RO 2018
Year: 2018

Wearable robotic hand rehabilitation devices can allow greater freedom and flexibility than their workstation-like counterparts. However, the field is generally lacking effective methods by which the user can operate the device: such controls must be effective, intuitive, and robust to the wide range of possible impairment patterns. Even when focusing on a specific condition, such as stroke, the variety of encountered upper limb impairment patterns means that a single sensing modality, such as electromyography (EMG), might not be sufficient to enable controls for a broad range of users. To address this significant gap, we introduce a multimodal sensing and interaction paradigm for an active hand orthosis. In our proof-of-concept implementation, EMG is complemented by other sensing modalities, such as finger bend and contact pressure sensors. We propose multimodal interaction methods that utilize this sensory data as input, and show they can enable tasks for stroke survivors who exhibit different impairment patterns. We believe that robotic hand orthoses developed as multimodal sensory platforms with help address some of the key challenges in physical interaction with the user.
Toward Multimodal Interaction in Scalable Visual Digital Evidence Visualization Using Computer Vision Techniques and ISS
Venue: arXiv cs.CV 2018
Year: 2018

Visualization requirements in Forensic Lucid have to do with different levels of case knowledge abstraction, representation, aggregation, as well as the operational aspects as the final long-term goal of this proposal. It encompasses anything from the finer detailed representation of hierarchical contexts to Forensic Lucid programs, to the documented evidence and its management, its linkage to programs, to evaluation, and to the management of GIPSY software networks. This includes an ability to arbitrarily switch between those views combined with usable multimodal interaction. The purpose is to determine how the findings can be applied to Forensic Lucid and investigation case management. It is also natural to want a convenient and usable evidence visualization, its semantic linkage and the reasoning machinery for it. Thus, we propose a scalable management, visualization, and evaluation of digital evidence using the modified interactive 3D documentary system - Illimitable Space System - (ISS) to represent, semantically link, and provide a usable interface to digital investigators that is navigable via different multimodal interaction techniques using Computer Vision techniques including gestures, as well as eye-gaze and audio.
Connectivity and Blockage Effects in Millimeter-Wave Air-To-Everything Networks
Venue: arXiv cs.IT 2018
Year: 2018

Millimeter-wave (mmWave) offers high data rate and bandwidth for air-to-everything (A2X) communications including air-to-air, air-to-ground, and air-to-tower. MmWave communication in the A2X network is sensitive to buildings blockage effects. In this paper, we propose an analytical framework to define and characterise the connectivity for an aerial access point (AAP) by jointly using stochastic geometry and random shape theory. The buildings are modelled as a Boolean line-segment process with fixed height. The blocking area for an arbitrary building is derived and minimized by optimizing the altitude of AAP. A lower bound on the connectivity probability is derived as a function of the altitude of AAP and different parameters of users and buildings including their densities, sizes, and heights. Our study yields guidelines on practical mmWave A2X networks deployment.
Tumor Delineation For Brain Radiosurgery by a ConvNet and Non-Uniform Patch Generation
Venue: arXiv cs.CV 2018
Year: 2018

Deep learning methods are actively used for brain lesion segmentation. One of the most popular models is DeepMedic, which was developed for segmentation of relatively large lesions like glioma and ischemic stroke. In our work, we consider segmentation of brain tumors appropriate to stereotactic radiosurgery which limits typical lesion sizes. These differences in target volumes lead to a large number of false negatives (especially for small lesions) as well as to an increased number of false positives for DeepMedic. We propose a new patch-sampling procedure to increase network performance for small lesions. We used a 6-year dataset from a stereotactic radiosurgery center. To evaluate our approach, we conducted experiments with the three most frequent brain tumors: metastasis, meningioma, schwannoma. In addition to cross-validation, we estimated quality on a hold-out test set which was collected several years later than the train one. The experimental results show solid improvements in both cases.
Category-level 6D Object Pose Recovery in Depth Images
Venue: arXiv cs.CV 2018
Year: 2018

Intra-class variations, distribution shifts among source and target domains are the major challenges of category-level tasks. In this study, we address category-level full 6D object pose estimation in the context of depth modality, introducing a novel part-based architecture that can tackle the above-mentioned challenges. Our architecture particularly adapts the distribution shifts arising from shape discrepancies, and naturally removes the variations of texture, illumination, pose, etc., so we call it as "Intrinsic Structure Adaptor (ISA)". We engineer ISA based on the followings: i) "Semantically Selected Centers (SSC)" are proposed in order to define the "6D pose" at the level of categories. ii) 3D skeleton structures, which we derive as shape-invariant features, are used to represent the parts extracted from the instances of given categories, and privileged one-class learning is employed based on these parts. iii) Graph matching is performed during training in such a way that the adaptation/generalization capability of the proposed architecture is improved across unseen instances. Experiments validate the promising performance of the proposed architecture on both synthetic and real datasets.
A Network Structure to Explicitly Reduce Confusion Errors in Semantic Segmentation
Venue: arXiv cs.CV 2018
Year: 2018

Confusing classes that are ubiquitous in real world often degrade performance for many vision related applications like object detection, classification, and segmentation. The confusion errors are not only caused by similar visual patterns but also amplified by various factors during the training of our designed models, such as reduced feature resolution in the encoding process or imbalanced data distributions. A large amount of deep learning based network structures has been proposed in recent years to deal with these individual factors and improve network performance. However, to our knowledge, no existing work in semantic image segmentation is designed to tackle confusion errors explicitly. In this paper, we present a novel and general network structure that reduces confusion errors in more direct manner and apply the network for semantic segmentation. There are two major contributions in our network structure: 1) We ensemble subnets with heterogeneous output spaces based on the discriminative confusing groups. The training for each subnet can distinguish confusing classes within the group without affecting unrelated classes outside the group. 2) We propose an improved cross-entropy loss function that maximizes the probability assigned to the correct class and penalizes the probabilities assigned to the confusing classes at the same time. Our network structure is a general structure and can be easily adapted to any other networks to further reduce confusion errors. Without any changes in the feature encoder and post-processing steps, our experiments demonstrate consistent and significant improvements on different baseline models on Cityscapes and PASCAL VOC datasets (e.g., 3.05% over ResNet-101 and 1.30% over ResNet-38).